Resilient nonlinear model predictive control for formation-containment of multi-mobile robot systems

IF 4.3 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Alireza Kazemi, Iman Sharifi
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引用次数: 0

Abstract

This paper focuses on resilient nonlinear model predictive control (NMPC) for the formation containment of multiple nonholonomic mobile robots in the presence of Denial-of-Service (DoS) attacks. The proposed strategy addresses obstacle and collision avoidance between agents by defining a safe circular region for each agent. The scenario-based cost function of NMPC encompasses terms dedicated to achieving the desired formation by leaders, converging the states of followers to the convex hull spanned by leaders, and minimizing control efforts. Utilizing an acknowledgment-based packet transmission strategy, coupled with a buffer mechanism on the actuator side, alleviates the impact of control signal absence during DoS attacks on the controller-to-actuator (C-A) channel. As a Lyapunov-based approach, the contractive constraint in MPC is employed to establish the stability of Multi-Robot Systems (MRS) throughout the mission. A search and rescue application, utilized as a simulation case study, verifies the proposed method’s usefulness and efficiency. Moreover, In the evaluation of real-time implementation, the proposed scheme was validated through a laboratory-based experiment involving a customized mobile robot and low-cost hardware-in-the-loop (HIL) agents based on Raspberry Pi.
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来源期刊
Robotics and Autonomous Systems
Robotics and Autonomous Systems 工程技术-机器人学
CiteScore
9.00
自引率
7.00%
发文量
164
审稿时长
4.5 months
期刊介绍: Robotics and Autonomous Systems will carry articles describing fundamental developments in the field of robotics, with special emphasis on autonomous systems. An important goal of this journal is to extend the state of the art in both symbolic and sensory based robot control and learning in the context of autonomous systems. Robotics and Autonomous Systems will carry articles on the theoretical, computational and experimental aspects of autonomous systems, or modules of such systems.
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